(For relevant technical literature, see the listing prior to the claims section).
Automatic and semiautomatic approaches to reconstruction and analysis of neuronal activity have been the subject of extensive research. A typical setup for a neuronal recording experiment in an animal or human subject requires high bandwidth communications between the recording electrodes and the processing computer, where spikes are detected and sorted. When a large number of recording electrodes is employed, typical transmission resources are insufficient and power-hungry. In addition, the large number of wires results in heavy cables, that severely constrain the subject. Consequently, it is desirable to pre-process and reduce the volume of the recorded data so that it can be transmitted wirelessly.
Investigation of implantable integrated circuits for power-efficient front-end processing of spikes is intended to minimize the communication bandwidth from the recording electrodes to the back-end computer. For instance, given a sampling rate of 24 Ksps and 12 bit sampling precision, the raw data rate is 288 Kbits/second per electrode. Spike detection and alignment (D&A) enables transmission of only active spike data and filters out the inter-spike noise. Assuming a high rate of 100 spikes/sec/electrode and 2 msec/spike, D&A reduces the data rate to 60 Kbits/sec. Spike sorting converts each spike to a short datagram (˜20 bits), reducing the required data rate down to 2 Kbits/sec per electrode, less than 1% of the original rate.
The computational task of this data reduction for signals acquired by tens or hundreds of electrodes typically requires special purpose computing hardware: A conventional computer CPU would either be too large, or dissipate too much power for an implantable or portable device, or would be too slow for the job. The special purpose hardware must implement a custom-tailored architecture that is carefully tuned to perform the desired algorithm.
The most limiting constraint on implantable chips for spike detection of many electrodes is power dissipation. While exact prediction of power requirements without completely designing the circuits is elusive, the computational complexity of several D&A algorithms has been investigated as a reasonable predictor of their power. The other figure of merit for D&A is the accuracy of subsequent spike processing, which depends heavily on the quality of D&A. Several algorithms and architectures are considered that trade off some subsequent classification accuracy in return for significant savings in power. The most favorable architecture is shown to achieve 99% of the accuracy of a “standard” algorithm, while incurring only 0.05% of its computational complexity.
Electrophysiological study of brain structures using wire electrodes is one of the oldest methods in neuroscience. A single electrode can often pick up signals of multiple neurons from a small region around its tip. Separation and sorting of action potential waveforms (“spikes”) originating from different neurons can be performed either on-line or off-line using various methods for pattern recognition. Off-line sorting is used for analysis of neural activity and also as a pre-requisite for on-line sorting. On-line sorting is used for closed loop experiments (in which stimulations are generated in response to detected spikes) and for clinical applications.
On-line sorting requires high bandwidth communications between the electrodes and the sorting computer, as well as high performance processing. When a large number of signals is to be handled, typical computing resources are insufficient. Special-purpose hardware for spike processing is called for high-volume research and clinical applications.
The prior art has demonstrated the feasibility of hardware implementation of two spike-sorting algorithms from a power consumption point of view. Yet, typical spike-sorting algorithms, such as based on Principal Component Analysis (PCA), are unattractive for efficient implementation in hardware, as they require storing and iterative processing of large amounts of data.
In typical neuronal recording experiments, the signals recorded by the electrodes are amplified and transmitted over wires to a host computer where they are digitized and processed according to the experimental requirements. The main disadvantage of that experimental arrangement is the need to connect a cable to the subject, restricting its movement.